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Improved machine learning models with a similarity-based approach for remaining useful life prediction

Published online by Cambridge University Press:  04 November 2024

F. Isbilen Open the ORCID record for F. Isbilen [Opens in a new window] ,
O. Bektas Open the ORCID record for O. Bektas [Opens in a new window] ,
R. Avsar Open the ORCID record for R. Avsar [Opens in a new window]  and
M. Konar Open the ORCID record for M. Konar [Opens in a new window]
F. Isbilen
Affiliation:
Aircraft Technology, Rumeli University, Istanbul, Türkiye
O. Bektas
Affiliation:
Faculty of Engineering and Natural Sciences, Istanbul Medeniyet University, Istanbul, Türkiye
R. Avsar
Affiliation:
Faculty of Engineering and Natural Sciences, Istanbul Medeniyet University, Istanbul, Türkiye
M. Konar*
Affiliation:
Faculty of Aeronautics and Astronautics, Erciyes University, Kayseri, Türkiye
*
Corresponding author: M. Konar; Email: [email protected]
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Abstract

Cost efficiency is a critical factor in the competitive aviation sector. These efficiency factors force airline operators to develop new approaches in their organizations. Predictive maintenance helps to build scheduling maintenance programs for airline operators or MROs. Scheduled maintenance programs benefit cost efficiency in the aviation sector. Predictive maintenance methods predict the failure time of any equipment. Predictions can be made by analyzing the sensor values from equipment.

In this paper, we predicted the remaining useful life (RUL) of turbofan engines using machine learning models and a similarity-based approach. Sensor datasets from the Prognostics Data Repository of NASA, called CMAPPS, were utilized. Using the FD0002 sub-dataset, a health index (HI) was created, and models were trained. Once the models were trained, train and test HIs were estimated. The predicted test HI was matched with the predicted train HI based on a similarity-based approach, and then a RUL prediction was made. The results obtained were compared with the actual results to calculate the accuracy, and the algorithm that resulted in the maximum accuracy was identified.

We selected six machine learning algorithms and also created an ensemble model by averaging the predictions of six machine learning algorithms for comparing prediction accuracy. The different algorithms were compared to obtain the prediction model with the closest prediction of remaining useful lifecycle in terms of the number of life cycles. This experiment showed us the effect of the similarity-based approach on the basic version of machine learning models for RUL prediction.

Keywords

CMAPPSPredictive maintenanceMachine learningSimilarity base
Type
Research Article
Information
The Aeronautical Journal , Volume 129 , Issue 1332 , February 2025 , pp. 485 - 505
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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